code: def parse_arch(arch, n_actions):
      if arch == "nature":
          return nn.Sequential(
              pnn.LargeAtariCNN(),
              init_chainer_default(nn.Linear(512, n_actions)),
              DiscreteActionValueHead(),
          )
      elif arch == "doubledqn":
          return nn.Sequential(
              pnn.LargeAtariCNN(),
              init_chainer_default(
                  nn.Linear(512, n_actions, bias=False)
              ),
              SingleSharedBias(),
              DiscreteActionValueHead(),
          )
      elif arch == "nips":
          return nn.Sequential(
              pnn.SmallAtariCNN(),
              init_chainer_default(nn.Linear(256, n_actions)),
              DiscreteActionValueHead(),
          )
      elif arch == "dueling":
          return DuelingDQN(n_actions)
      else:
          raise RuntimeError(
              "Not supported architecture: {}".format(arch)
          )
  
file_path: pfrl/examples/atari/train_dqn_ale.py
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import pfrl
  from pfrl import agents, experiments, explorers
  from pfrl import nn
  from pfrl import replay_buffers, utils
  from pfrl.initializers import init_chainer_default
  from pfrl.q_functions import DiscreteActionValueHead, DuelingDQN
  from pfrl.wrappers import atari_wrappers
label_desc: Use image node_id 1 for calling a global function with example usage: parse_arch(arch, n_actions) and returns: nn, nn, nn, DuelingDQN